This page evaluates RRTMGP-NN, an accelerated version of the RRTMGP gas-optics module that replaces the computational kernel of the original scheme with neural networks, keeping the spectral resolution of RRTMGP. It predicts the optical depths in each spectral interval, and is therefore different from some other neural network radiation schemes that predict fluxes directly. It is designed for weather and climate applications. The model used in this evaluation takes a large number of minor gases as input (all 16 non-constant RRTMGP long-wave gases), 9 of these are not included in CKDMIP and were set to zero. The comparisons below use the 50 profiles of the "Evaluation-1" CKDMIP dataset. The reference calculations were performed using LBLRTM to generate the high resolution absorption spectra and the CKDMIP software to perform the radiative transfer.
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